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Subcutaneous Lactate Correlates With Arterial Lactate in Severe Combined Trauma and Shock in Swine
Armaan S Somaney1, Lawrence A Renna1, John Weidling2
1Autonomous Reanimation and Evacuation (AREVA) Research Institute and Innovation Center, The Geneva Foundation, San Antonio, Texas; Department of Translational Medicine, University of the Incarnate Word School of Osteopathic Medicine, San Antonio, Texas.
Introduction:
Changes in blood lactate (Lac) carry prognostic value in pathologic processes and guide decision-making on therapeutic interventions. This feasibility study evaluated a new wearable continuous lactate monitor (CLM) currently undergoing preclinical validation in severe combat-relevant porcine trauma models.
Purpose:
Assess the performance of CLM compared to arterial blood gas (ABG) analysis.
Hypothesis:
CLM-derived Lac values correlate with ABG Lac.
Methods:
Female Yorkshire swine (50-60 kg, N = 6) were anesthetized, mechanically ventilated, subjected to a combination of the following injuries: bilateral pulmonary contusion (PC), traumatic brain injury (TBI), or hemorrhagic shock (HEM): (PC N = 3, PC + TBI N = 1; PC + HEM N = 1; PC + TBI + HEM N = 1). Animals were managed with immediate application of veno-venous extracorporeal life support. Two CLMs were placed in the skin of the lower abdomen. ABG analysis was performed using a GEM Premier 4000 [Werfen, Bedford, MA]. Raw CLM data were retrospectively calibrated using ABG values. Pearson Correlation Coefficient (P < 0.05) was calculated to compare CLM and ABG Lac values. Bland-Altman analysis was performed to assess device agreement before and after injury for up to 72 h.
Results:
Three hundred ninety-four paired measurements were accepted for analysis. Retrospectively calibrated CLM Lac correlated with ABG Lac, Pearson Correlation Coefficient = 0.687, P < 0.0001; however, a Bland-Altman analysis showed moderate agreement between the methods.
Conclusions:
CLM positively correlated with ABG Lac values, with moderate agreement between devices. Future optimization and utilization of CLM may significantly enhance sensor performance and reliability, making it suitable for monitoring and decision support during patient management.
